docs: def _transform_row(self, row, schema):
          """Apply the given schema to the given BigQuery data row.
  
          Parameters
          ----------
          row
              A single BigQuery row to transform
          schema : list
              The BigQuery table schema to apply to the row, specifically
              the list of field dicts.
  
          Returns
          -------
          dict
              Mapping schema to row
          """
  
          log = {}
  
          # Match each schema column with its associated row value
          for index, col_dict in enumerate(schema):
              col_name = col_dict['name']
              row_value = row['f'][index]['v']
  
              if row_value is None:
                  log[col_name] = None
                  continue
  
              # Recurse on nested records
              if col_dict['type'] == 'RECORD':
                  row_value = self._recurse_on_row(col_dict, row_value)
  
              # Otherwise just cast the value
              elif col_dict['type'] == 'INTEGER':
                  row_value = int(row_value)
  
              elif col_dict['type'] == 'FLOAT':
                  row_value = float(row_value)
  
              elif col_dict['type'] == 'BOOLEAN':
                  row_value = row_value in ('True', 'true', 'TRUE')
  
              elif col_dict['type'] == 'TIMESTAMP':
                  row_value = float(row_value)
  
              log[col_name] = row_value
  
          return log